Cauda EVs tRNAs analysis using data from both runs

Libraries required for data analysis

source("src.R")

Data: read counts table from both runs

tRNAs_3 <- read.table(
  file = "./run3_excerpt_counts/exceRpt_tRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)
tRNAs_3 <- as.data.frame(tRNAs_3)
counts_total_run3 <- tRNAs_3
tRNAs_4 <- read.table(
  file = "run4_excerpt_counts_/exceRpt_tRNA_ReadCounts.txt",
  header = TRUE, sep = "", row.names = 1
)

tRNAs_4 <- as.data.frame(tRNAs_4)

counts_total_run3 <- tRNAs_3
counts_total_run4 <- tRNAs_4

colnames(counts_total_run3) <- c(
  "MS28F1_sample11", "MS28F1_sample14",
  "MS28F1_sample2", "MS28F1_sample3",
  "MS28F1_sample4", "MS28F1_sample5",
  "MS28F1_sample6", "MS28F1_sample7",
  "MS28F1_sample8", "MS28F1_sample9"
)

colnames(counts_total_run4) <- c(
  "MS28F1_sample11_2", "MS28F1_sample14_2",
  "MS28F1_sample2_2", "MS28F1_sample3_2",
  "MS28F1_sample4_2", "MS28F1_sample5_2",
  "MS28F1_sample6_2", "MS28F1_sample7_2",
  "MS28F1_sample8_2", "MS28F1_sample9_2"
)


mlist <- list(counts_total_run3, counts_total_run4)
df <- mlist[[1]]
for (i in 2:length(mlist)) {
  df <- merge(df, mlist[[i]], by = "row.names", all = T)
  rownames(df) <- df$Row.names
  df <- df[, !(names(df) %in% "Row.names")]
  knitr::kable(head(df))
}

Specifying the group & batch while filterByExprs()

counts_total <- df
d <- data.frame(
  row.names = colnames(counts_total),
  group = c(
    "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1", "G2",
    "G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
  ),
  batch = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
)
counts_total[is.na(counts_total)] <- 0
dds <- calcNormFactors(DGEList(counts_total), method = "TMM")
dds$samples$group <- c(
  "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1", "G1",
  "G2", "G1", "G2", "G2", "G1", "G2", "G1", "G2", "G1", "G2", "G1"
)
dds$samples$batch <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
mm <- model.matrix(~ batch + group, data = d)

dds <- dds[which(filterByExpr(dds, design = mm, min.count = 10, min.prop = 0.8)), ]
en <- log1p(edgeR::cpm(dds)) # normalization by counts per million

Differential Expression analysis without Surrogate Variable analysis

dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_without_ruvseq <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_without_ruvseq, n = 20))
logFC logCPM LR PValue FDR
Tyr -0.4893745 7.935688 3.8666022 0.0492561 0.5421865
Ile -0.4297720 9.694536 3.0391174 0.0812801 0.5421865
Cys 0.2972731 12.607438 2.6750203 0.1019340 0.5421865
Asp -0.2428079 12.437206 2.0769683 0.1495369 0.5421865
Glu 0.2244529 17.474000 1.9452016 0.1631049 0.5421865
SeC 0.2683224 10.280610 1.8293835 0.1762004 0.5421865
Pro 0.2254161 9.635737 1.7309576 0.1882886 0.5421865
Val 0.3290303 14.720771 1.6632971 0.1971587 0.5421865
Ser 0.2423846 10.004283 1.2642794 0.2608426 0.6376153
Gly 0.1848298 19.674351 1.0426427 0.3072076 0.6758568
Arg -0.1978367 10.647809 0.8140622 0.3669220 0.7338440
iMet 0.1298134 11.672785 0.5572120 0.4553858 0.8348739
Gln 0.1184996 10.309014 0.2377104 0.6258647 0.9179376
His 0.1008914 12.581947 0.1950451 0.6587504 0.9179376
Lys -0.0569287 14.572036 0.1313012 0.7170867 0.9179376
Leu 0.0523660 10.795842 0.0894270 0.7649068 0.9179376
Ala 0.0545848 10.771378 0.0837683 0.7722542 0.9179376
Asn -0.0582296 9.239539 0.0459426 0.8302802 0.9179376
Thr -0.0424330 9.346387 0.0205495 0.8860129 0.9179376
Phe 0.0314313 6.912327 0.0183823 0.8921522 0.9179376

PCA plots without Surrogate Variable analysis

All samples (normalized by counts-per-million), colored by treatment group

plgINS::plPCA(en,
  colorBy = dds$samples$group,
  add.labels = FALSE, points.size = 15
)

All samples (normalized by counts-per-million), colored by library size

Note: Even after the normalization, the libraries are separated based on lib.size, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary ###

plgINS::plPCA(en,
  colorBy = dds$samples$lib.size,
  add.labels = FALSE, points.size = 15
)

All samples (normalized by counts-per-million), colored by sequencing batch

Note: Even after the normalization, the libraries are separated based on the sequencing batch, without surrogate variable analysis Therefore, Surrogate variable analysis is necessary

plgINS::plPCA(en,
  colorBy = dds$samples$batch,
  add.labels = FALSE, points.size = 15
)

Differential Expression analysis with Surrogate Variable analysi, SV1

re <- RUVSeq::RUVs(en,
  cIdx = row.names(en), k = 1,
  scIdx = RUVSeq::makeGroups(d$group), isLog = TRUE
)
d$SV1 <- re$W[, 1] # add the variable (suppose I have only one) to the colData dataframe
mm <- model.matrix(~ SV1 + batch + group, data = d)
dds <- estimateDisp(dds, mm)
fit <- glmLRT(glmFit(dds, mm), coef = "groupG2")
res_with_ruvseq1 <- as.data.frame(topTags(fit, Inf))
knitr::kable(head(res_with_ruvseq1, n = 20))
logFC logCPM LR PValue FDR
Cys 0.3149355 12.610101 3.6056944 0.0575820 0.3416520
Ile -0.2592603 9.666343 3.5504357 0.0595298 0.3416520
SeC 0.3189241 10.290391 3.5327978 0.0601662 0.3416520
Tyr -0.3235718 7.874531 3.4798988 0.0621185 0.3416520
Pro 0.2385796 9.632674 2.5146838 0.1127903 0.4763612
Glu 0.2060507 17.474065 2.0768625 0.1495473 0.4763612
Asp -0.1918334 12.442074 2.0563839 0.1515695 0.4763612
Ser 0.2383967 9.973714 1.3190278 0.2507666 0.6197481
Ala 0.1386194 10.757208 1.2480900 0.2639176 0.6197481
Val 0.2605593 14.721542 1.1445959 0.2846833 0.6197481
Gly 0.1677909 19.674375 1.0240802 0.3115531 0.6197481
iMet 0.1492808 11.662440 0.9178337 0.3380444 0.6197481
Asn 0.1740746 9.227860 0.7597973 0.3833920 0.6365502
Trp 0.1403309 9.786448 0.6458976 0.4215832 0.6365502
Thr 0.1810413 9.332521 0.6120646 0.4340115 0.6365502
His 0.1343053 12.584365 0.3713364 0.5422767 0.7216184
Leu 0.0895625 10.782906 0.3438519 0.5576142 0.7216184
Gln 0.1270955 10.295692 0.2756150 0.5995898 0.7245608
Arg -0.0585177 10.635208 0.2378586 0.6257571 0.7245608
Phe 0.0833018 6.792185 0.1871275 0.6653182 0.7318501

PCA plots with Surrogate variable analysis, SV=1

All samples, colored by treatment group

plgINS::plPCA(re$normalizedCounts,
  colorBy = d$group,
  add.labels = FALSE, points.size = 15
)

All samples, colored by library size

Note: We can observe that the samples are clustering more by groups & spread by counts. Libraries with high/low counts are not clustered together anymore. No effect of library size, keeping the group effect:

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$lib.size,
  add.labels = FALSE, points.size = 15
)

All samples, colored by sequencing batch

Note: We can observe that the samples are corrected for sequencing batch effect:

plgINS::plPCA(re$normalizedCounts,
  colorBy = dds$samples$batch,
  add.labels = FALSE, points.size = 15
)

References

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SessionInfo

devtools::session_info() %>%
  details::details()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.4 (2021-02-15)
 os       Ubuntu 16.04.7 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  de_DE.UTF-8                 
 ctype    de_DE.UTF-8                 
 tz       Europe/Zurich               
 date     2021-06-28                  

─ Packages ───────────────────────────────────────────────────────────────────
 ! package                * version  date       lib source        
   annotate                 1.68.0   2020-10-27 [1] Bioconductor  
   AnnotationDbi          * 1.52.0   2020-10-27 [1] Bioconductor  
   AnnotationFilter         1.14.0   2020-10-27 [1] Bioconductor  
   AnnotationHub            2.22.1   2021-04-16 [1] Bioconductor  
   aroma.light              3.20.0   2020-10-27 [1] Bioconductor  
   ash                      1.0-15   2015-09-01 [1] CRAN (R 4.0.4)
   askpass                  1.1      2019-01-13 [1] CRAN (R 4.0.4)
   assertthat               0.2.1    2019-03-21 [1] CRAN (R 4.0.4)
   backports                1.2.1    2020-12-09 [1] CRAN (R 4.0.4)
   bayestestR               0.9.0    2021-04-08 [1] CRAN (R 4.0.4)
   beeswarm                 0.3.1    2021-03-07 [1] CRAN (R 4.0.4)
   Biobase                * 2.50.0   2020-10-27 [1] Bioconductor  
   BiocFileCache            1.14.0   2020-10-27 [1] Bioconductor  
   BiocGenerics           * 0.36.1   2021-04-16 [1] Bioconductor  
   BiocManager              1.30.12  2021-03-28 [1] CRAN (R 4.0.4)
   BiocParallel           * 1.24.1   2020-11-06 [1] Bioconductor  
   BiocVersion              3.12.0   2020-04-27 [1] Bioconductor  
   biomaRt                * 2.46.3   2021-02-09 [1] Bioconductor  
   Biostrings             * 2.58.0   2020-10-27 [1] Bioconductor  
   bit                      4.0.4    2020-08-04 [1] CRAN (R 4.0.4)
   bit64                    4.0.5    2020-08-30 [1] CRAN (R 4.0.4)
   bitops                   1.0-7    2021-04-24 [1] CRAN (R 4.0.4)
   blob                     1.2.1    2020-01-20 [1] CRAN (R 4.0.4)
   bookdown                 0.22     2021-04-22 [1] CRAN (R 4.0.4)
   boot                     1.3-27   2021-02-12 [1] CRAN (R 4.0.4)
   broom                    0.7.7    2021-06-13 [1] CRAN (R 4.0.4)
   bslib                    0.2.4    2021-01-25 [1] CRAN (R 4.0.4)
   cachem                   1.0.4    2021-02-13 [1] CRAN (R 4.0.4)
   callr                    3.7.0    2021-04-20 [1] CRAN (R 4.0.4)
   caTools                  1.18.2   2021-03-28 [1] CRAN (R 4.0.4)
   cellranger               1.1.0    2016-07-27 [1] CRAN (R 4.0.4)
   cli                      2.5.0    2021-04-26 [1] CRAN (R 4.0.4)
   clipr                    0.7.1    2020-10-08 [1] CRAN (R 4.0.4)
   coda                     0.19-4   2020-09-30 [1] CRAN (R 4.0.4)
   codetools                0.2-18   2020-11-04 [1] CRAN (R 4.0.4)
   colorRamps               2.3      2012-10-29 [1] CRAN (R 4.0.4)
   colorspace               2.0-0    2020-11-11 [1] CRAN (R 4.0.4)
   conquer                  1.0.2    2020-08-27 [1] CRAN (R 4.0.4)
   cqn                    * 1.36.0   2020-10-27 [1] Bioconductor  
   crayon                   1.4.1    2021-02-08 [1] CRAN (R 4.0.4)
   crosstalk                1.1.1    2021-01-12 [1] CRAN (R 4.0.4)
   curl                     4.3      2019-12-02 [1] CRAN (R 4.0.4)
   data.table               1.14.0   2021-02-21 [1] CRAN (R 4.0.4)
   DBI                      1.1.1    2021-01-15 [1] CRAN (R 4.0.4)
   dbplyr                   2.1.1    2021-04-06 [1] CRAN (R 4.0.4)
   DelayedArray             0.16.3   2021-03-24 [1] Bioconductor  
   desc                     1.3.0    2021-03-05 [1] CRAN (R 4.0.4)
   DESeq2                 * 1.30.1   2021-02-19 [1] Bioconductor  
   details                  0.2.1    2020-01-12 [1] CRAN (R 4.0.4)
   devtools                 2.4.2    2021-06-07 [1] CRAN (R 4.0.4)
   digest                   0.6.27   2020-10-24 [1] CRAN (R 4.0.4)
   doParallel               1.0.16   2020-10-16 [1] CRAN (R 4.0.4)
   dplyr                  * 1.0.5    2021-03-05 [1] CRAN (R 4.0.4)
   EDASeq                 * 2.24.0   2020-10-27 [1] Bioconductor  
   edgeR                  * 3.32.1   2021-01-14 [1] Bioconductor  
   effectsize               0.4.4-1  2021-04-05 [1] CRAN (R 4.0.4)
   ellipsis                 0.3.1    2020-05-15 [1] CRAN (R 4.0.4)
   emmeans                  1.6.0    2021-04-24 [1] CRAN (R 4.0.4)
   EnhancedVolcano        * 1.8.0    2020-10-27 [1] Bioconductor  
   ensembldb                2.14.1   2021-04-19 [1] Bioconductor  
   estimability             1.3      2018-02-11 [1] CRAN (R 4.0.4)
   evaluate                 0.14     2019-05-28 [1] CRAN (R 4.0.4)
   extrafont                0.17     2014-12-08 [1] CRAN (R 4.0.4)
   extrafontdb              1.0      2012-06-11 [1] CRAN (R 4.0.4)
   fansi                    0.4.2    2021-01-15 [1] CRAN (R 4.0.4)
   farver                   2.1.0    2021-02-28 [1] CRAN (R 4.0.4)
   fastmap                  1.1.0    2021-01-25 [1] CRAN (R 4.0.4)
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[1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/4.0
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library

 P ── Loaded and on-disk path mismatch.